Hsu Hsi-Chou, Chen Wei-Hsin, Lin Yi-Wen, Huang Yung-Fa
Department of Computer and Communication, National Pingtung University, Pingtung 91201, Taiwan.
Department of Computer Science and Information Engineering, National Pingtung University, Pingtung 91201, Taiwan.
Sensors (Basel). 2025 Apr 3;25(7):2267. doi: 10.3390/s25072267.
Non-contact human respiration rate monitoring can be used for sleep apnea detection and home care. Typically, the human body does not remain stationary for long periods, and body movement can significantly affect the performance of non-contact respiratory monitoring. Because the breathing rate generally remains stable over short periods, using measurements from only a portion of the radar echo signals does not result in significant errors, and these errors will be smaller than those caused by body movement. However, selecting a window size that is too short reduces frequency resolution, leading to increased estimation errors. Choosing an appropriate window length can improve estimation accuracy. In this paper, we propose an algorithm to determine whether the subject is stationary and select the received signal with minimal body movement. Experimental results are compared using alternative schemes, including fast Fourier transform (FFT), short-time Fourier transform (STFT), and RGB-D camera-assisted methods, in terms of root mean square error (RMSE) performance.
非接触式人体呼吸率监测可用于睡眠呼吸暂停检测和家庭护理。通常，人体不会长时间保持静止，身体运动可能会显著影响非接触式呼吸监测的性能。由于呼吸率在短时间内通常保持稳定，仅使用一部分雷达回波信号进行测量不会导致显著误差，并且这些误差将小于身体运动引起的误差。然而，选择过短的窗口大小会降低频率分辨率，导致估计误差增加。选择合适的窗口长度可以提高估计精度。在本文中，我们提出了一种算法，用于确定受试者是否静止，并选择身体运动最小的接收信号。使用包括快速傅里叶变换（FFT）、短时傅里叶变换（STFT）和RGB-D相机辅助方法在内的替代方案，根据均方根误差（RMSE）性能对实验结果进行了比较。
